# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os
import psutil
import warnings
from dataclasses import dataclass, field
from typing import Optional, List
from functools import wraps

import trl
import inspect
from trl import SFTTrainer

# why: bypass partially-initialised unsloth ns during _gpu_init load
from .models._utils import is_bfloat16_supported
from unsloth.utils import (
    configure_padding_free,
    configure_sample_packing,
    enable_padding_free_metadata,
    enable_sample_packing,
)
from unsloth_zoo.training_utils import (
    unsloth_train as _unsloth_train,
)
from unsloth_zoo.vision_utils import (
    UnslothVisionDataCollator as _UnslothVisionDataCollatorBase,
)
from unsloth.models.vision import check_dataset_for_missing_videos
from unsloth_zoo.hf_utils import get_transformers_model_type
from unsloth_zoo.utils import Version
import dataclasses

__all__ = [
    "UnslothTrainingArguments",
    "UnslothTrainer",
    "unsloth_train",
    "_patch_trl_trainer",
    "UnslothVisionDataCollator",
    "QGaloreConfig",
    "check_dataset_for_missing_videos",
]

logger = logging.getLogger(__name__)


class UnslothVisionDataCollator(_UnslothVisionDataCollatorBase):
    """
    Drop-in zoo collator that validates local video paths on every batch
    (deduped across batches), applying formatting_func first so formatter-made
    paths are checked too. Raises FileNotFoundError on missing files instead
    of silently training on empty video tensors (issue #5085).
    """

    __slots__ = ("_checked_video_paths",)

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._checked_video_paths = set()

    def __call__(self, examples):
        formatting_func = self.formatting_func
        if formatting_func is not None:
            examples = [formatting_func(example) for example in examples]

        check_dataset_for_missing_videos(
            examples,
            raise_error = True,
            checked = self._checked_video_paths,
        )

        if formatting_func is None:
            return super().__call__(examples)

        # why: base __call__ would reapply formatting_func; applied above.
        self.formatting_func = None
        try:
            return super().__call__(examples)
        finally:
            self.formatting_func = formatting_func


_AUTO_PADDING_FREE_ENV_DISABLED = os.environ.get(
    "UNSLOTH_DISABLE_AUTO_PADDING_FREE", ""
).strip().lower() in {"1", "true", "yes", "on"}

PADDING_FREE_BLOCKLIST = {
    "gemma2",  # - gemma2:  Uses slow_attention_softcapping which has torch.compile issues
    "gpt_oss",  # - gpt_oss: Uses Flex Attention which doesn't handle padding_free correctly
}


def _should_pack(config) -> bool:
    if config is None or not getattr(config, "packing", False):
        return False
    return not getattr(config, "_unsloth_disable_auto_packing", False)


def _should_auto_padding_free(config) -> bool:
    if config is None or _AUTO_PADDING_FREE_ENV_DISABLED or getattr(config, "packing", False):
        return False
    return getattr(config, "padding_free", None) is None


def _disable_sample_packing(config):
    if config is None:
        return
    for attr, value in (("packing", False), ("padding_free", False)):
        if hasattr(config, attr):
            setattr(config, attr, value)
    if hasattr(config, "remove_unused_columns"):
        setattr(config, "remove_unused_columns", True)
    setattr(config, "_unsloth_disable_auto_packing", True)


_AUTO_PACK_SKIP_MESSAGES = (
    "packing is not supported",
    "padding-free training",
    "passing a custom data collator",
)


def _should_skip_auto_packing_error(exc: Exception) -> bool:
    message = str(exc).lower()
    return any(msg in message for msg in _AUTO_PACK_SKIP_MESSAGES)


# Unsloth gradient accumulation fix:
from transformers import __version__ as transformers_version, ProcessorMixin

if Version(transformers_version) > Version("4.45.2"):

    def unsloth_train(trainer, *args, **kwargs):
        return trainer.train(*args, **kwargs)

else:

    def unsloth_train(trainer, *args, **kwargs):
        if len(args) != 0 or len(kwargs) != 0:
            raise RuntimeError(
                "Unsloth: Our custom gradient accumulation fixed trainer does not support other arguments.\n"
                "If you want to use our fix inside of HF, please update `transformers` to the latest version via:\n"
                "`pip uninstall transformers -y && pip install --upgrade --no-cache-dir transformers`"
            )
        print(
            "Unsloth: Using our custom gradient accumulation fixed trainer, which is not feature complete.\n"
            "If you want to use our fix inside of HF, please update `transformers` to the latest version via:\n"
            "`pip uninstall transformers -y && pip install --upgrade --no-cache-dir transformers`"
        )
        return _unsloth_train(trainer)


try:
    from trl import SFTConfig as TrainingArguments
except:
    from transformers import TrainingArguments


@dataclass
class QGaloreConfig:
    """Configuration for Q-GaLore optimizer integration.

    Pass an instance of this class to ``UnslothTrainingArguments`` (via
    ``q_galore_config``) to enable Q-GaLore training.
    """

    rank: int = 256
    update_proj_gap: int = 200
    scale: float = 0.25
    proj_quant: bool = True
    proj_quant_group_size: int = -1
    proj_quant_n_bit: int = 4
    weight_quant: bool = False
    stochastic_round: bool = True
    weight_group_size: int = 128
    cos_threshold: float = 0.4
    gamma_proj: float = 2.0
    queue_size: int = 5
    target_modules: Optional[List[str]] = None


class UnslothTrainingArguments(TrainingArguments):
    def __init__(
        self,
        embedding_learning_rate: float = None,
        q_galore_config: Optional[QGaloreConfig] = None,
        *args,
        **kwargs,
    ):
        self.q_galore_config = q_galore_config
        self.embedding_learning_rate = embedding_learning_rate
        super().__init__(*args, **kwargs)
        self.embedding_learning_rate = embedding_learning_rate


def _create_unsloth_optimizer(
    model,
    optimizer_cls,
    optimizer_kwargs,
    embedding_lr = 5e-5,
):
    lr = optimizer_kwargs["lr"]
    weight_decay = optimizer_kwargs.get("weight_decay", 0.0)

    param_groups = {
        "non_embeddings": {},
        "embeddings": {},
    }

    for name, param in model.named_parameters():
        if not param.requires_grad:
            continue
        if name.endswith("modules_to_save.default.weight"):
            partial_name = name[: -len(".modules_to_save.default.weight")]
            partial_name = partial_name[partial_name.rfind(".") + 1 :]
            print(
                f"Unsloth: Setting lr = {embedding_lr:.2e} instead of {lr:.2e} for {partial_name}."
            )
            param_groups["embeddings"][name] = param
        else:
            param_groups["non_embeddings"][name] = param

    optimizer_grouped_parameters = [
        {
            "params": list(param_groups["non_embeddings"].values()),
            "weight_decay": weight_decay,
            "lr": lr,
        },
        {
            "params": list(param_groups["embeddings"].values()),
            "weight_decay": weight_decay,
            "lr": embedding_lr,
        },
    ]
    optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
    return optimizer


class UnslothTrainer(SFTTrainer):
    def create_optimizer(self):
        # --- Q-GaLore optimizer ---
        q_galore_config = getattr(self.args, "q_galore_config", None)
        if q_galore_config is not None and self.optimizer is None:
            embedding_lr = getattr(self.args, "embedding_learning_rate", None)
            return self._create_q_galore_optimizer(q_galore_config, embedding_lr)

        # --- Embedding-LR optimizer ---
        embedding_learning_rate = getattr(self.args, "embedding_learning_rate", None)
        if embedding_learning_rate is None:
            return super().create_optimizer()

        if self.optimizer is None:
            optimizer_cls, optimizer_kwargs = SFTTrainer.get_optimizer_cls_and_kwargs(self.args)
            self.optimizer = _create_unsloth_optimizer(
                self.model,
                optimizer_cls,
                optimizer_kwargs,
                embedding_learning_rate,
            )
        return self.optimizer

    def _create_q_galore_optimizer(
        self,
        config: "QGaloreConfig",
        embedding_lr = None,
    ):
        """Build the Q-GaLore optimizer from a QGaloreConfig."""
        from unsloth.optimizers.q_galore_adamw import (
            QGaLoreAdamW8bit,
            make_q_galore_param_groups,
            install_weight_quant_hooks,
        )

        lr = self.args.learning_rate
        weight_decay = self.args.weight_decay

        param_groups = make_q_galore_param_groups(
            self.model,
            lr = lr,
            weight_decay = weight_decay,
            rank = config.rank,
            update_proj_gap = config.update_proj_gap,
            scale = config.scale,
            proj_quant = config.proj_quant,
            proj_quant_group_size = config.proj_quant_group_size,
            proj_quant_n_bit = config.proj_quant_n_bit,
            weight_quant = config.weight_quant,
            stochastic_round = config.stochastic_round,
            weight_group_size = config.weight_group_size,
            cos_threshold = config.cos_threshold,
            gamma_proj = config.gamma_proj,
            queue_size = config.queue_size,
            target_modules = config.target_modules,
        )

        # --- Split embedding params with custom LR (Fix #2) ---
        if embedding_lr is not None:
            # Fast param->name lookup (O(N) instead of O(N*M))
            param_to_name = {id(p): name for name, p in self.model.named_parameters()}

            new_groups = []
            for group in param_groups:
                if "rank" in group:
                    # GaLore group: keep as-is (no embeddings here)
                    new_groups.append(group)
                    continue
                # Non-GaLore group: split out embedding params
                embed_params = []
                other_params = []
                for p in group["params"]:
                    name = param_to_name.get(id(p))
                    if name and name.endswith("modules_to_save.default.weight"):
                        partial_name = name[: -len(".modules_to_save.default.weight")]
                        partial_name = partial_name[partial_name.rfind(".") + 1 :]
                        print(
                            f"Unsloth: Setting lr = {embedding_lr:.2e} instead of {lr:.2e} for {partial_name}."
                        )
                        embed_params.append(p)
                    else:
                        other_params.append(p)
                if other_params:
                    other_group = dict(group)
                    other_group["params"] = other_params
                    new_groups.append(other_group)
                if embed_params:
                    embed_group = dict(group)
                    embed_group["params"] = embed_params
                    embed_group["lr"] = embedding_lr
                    new_groups.append(embed_group)
            param_groups = new_groups

        # --- Forward optimizer hyperparameters (Fix #3) ---
        self.optimizer = QGaLoreAdamW8bit(
            param_groups,
            lr = lr,
            weight_decay = weight_decay,
            betas = (self.args.adam_beta1, self.args.adam_beta2),
            eps = self.args.adam_epsilon,
        )

        if config.weight_quant:
            QGaLoreAdamW8bit.init_weight_quantization(
                self.model,
                param_groups,
                group_size = config.weight_group_size,
                stochastic = config.stochastic_round,
            )
            # Pre-hooks dequantize INT8 weights to float before each forward,
            # letting the optimizer free float weight memory between steps.
            install_weight_quant_hooks(self.model)

        n_galore = sum(len(g["params"]) for g in param_groups if "rank" in g)
        n_other = sum(len(g["params"]) for g in param_groups if "rank" not in g)
        print(
            f"🦥 Unsloth: Q-GaLore enabled — "
            f"{n_galore} GaLore params (rank={config.rank}), "
            f"{n_other} standard params."
        )

        return self.optimizer


# From `trl>=0.13.0`, they changed how to pass several params to the trainer
# We need to patch to make the transition smooth
def _resolve_trainer_params(trainer_class, init_fn):
    """Resolve the real named parameters for a trainer __init__.

    Some TRL trainers are thin ``*args, **kwargs`` wrappers; for those, walk the
    MRO and return the first parent with real named parameters.
    """
    params = inspect.signature(init_fn).parameters
    named = {
        k
        for k, v in params.items()
        if v.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY)
        and k != "self"
    }
    if named:
        return set(params.keys())

    # Thin wrapper detected - walk MRO for real signature
    for cls in trainer_class.__mro__[1:]:
        if cls is object:
            continue
        parent_init = cls.__dict__.get("__init__")
        if parent_init is None:
            continue
        try:
            parent_params = inspect.signature(parent_init).parameters
            parent_named = {
                k
                for k, v in parent_params.items()
                if v.kind
                in (
                    inspect.Parameter.POSITIONAL_OR_KEYWORD,
                    inspect.Parameter.KEYWORD_ONLY,
                )
                and k != "self"
            }
            if parent_named:
                return set(parent_params.keys())
        except (ValueError, TypeError):
            continue
    return set(params.keys())


def _backwards_compatible_trainer(trainer_class, config_class):
    original_init = trainer_class.__init__

    @wraps(original_init)
    def new_init(self, *args, **kwargs):
        # tokenizer is now processing_class
        trainer_params = _resolve_trainer_params(trainer_class, original_init)

        if "processing_class" in trainer_params and "tokenizer" in kwargs:
            kwargs["processing_class"] = kwargs.pop("tokenizer")

        if ("args" in kwargs) and (Version(trl) >= Version("0.13.0.dev0")):
            training_args = kwargs.pop("args", None)

            trainer_params.remove("self")
            trainer_params.remove("args")

            # Fields that should be passed to Config init
            config_fields = {
                field.name: field for field in dataclasses.fields(config_class) if field.init
            }

            config_dict = {
                name: getattr(training_args, name)
                for name in config_fields
                if hasattr(training_args, name)
            }

            # Params in Config but not in TrainingArguments
            from transformers import TrainingArguments

            moved_params = set(inspect.signature(config_class).parameters.keys()) - set(
                inspect.signature(TrainingArguments).parameters.keys()
            )

            # Separate kwargs into trainer kwargs and config kwargs
            trainer_kwargs = {}
            additional_config_kwargs = {}

            for key, value in kwargs.items():
                if key in trainer_params:
                    trainer_kwargs[key] = value
                elif key in moved_params or key in config_fields:
                    additional_config_kwargs[key] = value
                else:
                    additional_config_kwargs[key] = value

            config_dict.update(additional_config_kwargs)

            # Only build the config if the previous init wasn't TrainingArguments:
            # reinitialising it would re-trigger mutually-exclusive param checks.
            # See https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_config.py#L499-L502
            if not isinstance(training_args, TrainingArguments):
                config = config_class(**config_dict)
            else:
                config = training_args

            # Reconstruct kwargs for Trainer
            kwargs = trainer_kwargs
            kwargs["args"] = config
        original_init(self, *args, **kwargs)

    return new_init


def _patch_sft_trainer_auto_packing(trl_module):
    sft_trainer = getattr(trl_module, "SFTTrainer", None)
    if sft_trainer is None:
        return
    if getattr(sft_trainer, "_unsloth_auto_packing_wrapped", False):
        return

    original_init = sft_trainer.__init__

    @wraps(original_init)
    def new_init(self, *args, **kwargs):
        config_arg = None
        if len(args) >= 2:
            config_arg = args[1]
        else:
            config_arg = kwargs.get("args")

        model = kwargs.get("model")
        is_unsupported_model = False
        is_vlm = False
        if model is not None:
            model_config = getattr(model, "config", None)
            if model_config is not None:
                model_types = get_transformers_model_type(model_config)
                is_unsupported_model = any(x in PADDING_FREE_BLOCKLIST for x in model_types)

                architectures = getattr(model_config, "architectures", None)
                if architectures is None:
                    architectures = []
                is_vlm = any(x.endswith("ForConditionalGeneration") for x in architectures)
                is_vlm = is_vlm or hasattr(model_config, "vision_config")

        processing_class = kwargs.get("processing_class") or kwargs.get("tokenizer")
        data_collator = kwargs.get("data_collator")

        # Disable padding-free for VLMs / custom collators / blocklisted models
        blocked = (
            (data_collator is not None)
            or isinstance(processing_class, ProcessorMixin)
            or is_vlm
            or is_unsupported_model
            or (
                os.environ.get("UNSLOTH_RETURN_LOGITS", "0") == "1"
            )  # Disable padding free on forced logits
        )
        requested_pack = bool(getattr(config_arg, "packing", False))
        if blocked:
            if hasattr(config_arg, "packing"):
                setattr(config_arg, "packing", False)
            if hasattr(config_arg, "padding_free"):
                setattr(config_arg, "padding_free", False)

        if blocked and requested_pack:
            reason = "custom data collator"
            if data_collator is None and isinstance(processing_class, ProcessorMixin):
                reason = "processor-based model"
            elif is_vlm:
                reason = "vision-language model"
            elif is_unsupported_model:
                reason = f"unsupported model type(s): {', '.join(model_types)}"
            message = f"Unsloth: Sample packing skipped ({reason} detected)."
            print(message)

        packing_active = False
        if _should_pack(config_arg) and not blocked:
            configure_sample_packing(config_arg)
            packing_active = True
            logger.info("Unsloth: Sample packing enabled for SFTTrainer instance.")

        # Resolve padding_free: None (default) = auto-enable unless env-disabled or packing
        auto_padding_free_active = False
        padding_free_requested = getattr(config_arg, "padding_free", None) is True
        if not blocked:
            if padding_free_requested:
                configure_padding_free(config_arg)
            elif _should_auto_padding_free(config_arg):
                configure_padding_free(config_arg)
                auto_padding_free_active = True
                logger.info("Unsloth: Padding-free batching auto-enabled for SFTTrainer instance.")

        try:
            original_init(self, *args, **kwargs)
        except ValueError as exc:
            if packing_active and _should_skip_auto_packing_error(exc):
                logger.info(
                    "Unsloth: Auto sample packing failed because trainer reported an incompatible setup (%s).",
                    exc,
                )
                _disable_sample_packing(config_arg)
                packing_active = False
                original_init(self, *args, **kwargs)
            else:
                raise

        trainer_args = getattr(self, "args", None)
        trainer_packing = bool(trainer_args and getattr(trainer_args, "packing", False))
        trainer_padding_free = bool(trainer_args and getattr(trainer_args, "padding_free", False))

        if blocked and trainer_args is not None:
            # Mirror the block on the trainer args to avoid re-enabling later
            setattr(trainer_args, "packing", False)
            setattr(trainer_args, "padding_free", False)

        if not blocked and trainer_packing and (packing_active or _should_pack(trainer_args)):
            enable_sample_packing(self.model, self)
            print("🦥 Unsloth: Packing enabled - training is >2x faster and uses less VRAM!")
        elif not blocked and trainer_padding_free:
            enable_padding_free_metadata(self.model, self)
            message = (
                "🦥 Unsloth: Padding-free auto-enabled, enabling faster training."
                if auto_padding_free_active
                else "🦥 Unsloth: Padding-free enabled, enabling faster training."
            )
            print(message)

        # get_peft_model installs a pre-train forward detector for plain LoRA/vision models,
        # but only RL trainers run the reset via prepare_for_training_mode. Wire it into the
        # SFT train() path too, else a grad-enabled probe before train() leaves the poisoned
        # Dynamo cache in place and the detector hook installed on every training forward.
        # (For UnslothSFTTrainer the later prepare_for_training_mode assignment supersedes this.)
        if not getattr(self, "_unsloth_train_reset_wrapped", False):
            try:
                from unsloth.models._utils import _unsloth_reset_stray_compile_cache

                _orig_train = self.train

                @wraps(_orig_train)
                def _train_with_reset(*train_args, **train_kwargs):
                    try:
                        _unsloth_reset_stray_compile_cache(self)
                    except Exception:
                        pass
                    return _orig_train(*train_args, **train_kwargs)

                self.train = _train_with_reset
                self._unsloth_train_reset_wrapped = True
            except Exception:
                pass

    sft_trainer.__init__ = new_init
    sft_trainer._unsloth_auto_packing_wrapped = True


def _patch_trl_trainer():
    import trl

    if hasattr(trl, "__UNSLOTH_BACKWARDS_COMPATIBLE__"):
        return
    if Version(trl) <= Version("0.11.0"):
        return

    import trl.trainer

    trl_classes = dir(trl.trainer)
    trl_trainers = set(x[: -len("Trainer")] for x in trl_classes if x.endswith("Trainer"))
    trl_configs = set(x[: -len("Config")] for x in trl_classes if x.endswith("Config"))
    trl_classes = list(trl_trainers & trl_configs)

    for x in trl_classes:
        try:
            exec(
                f"trl.{x}Trainer.__init__ = _backwards_compatible_trainer(trl.{x}Trainer, trl.{x}Config)",
                globals(),
            )
        except:
            continue

    _patch_sft_trainer_auto_packing(trl)

    trl.__UNSLOTH_BACKWARDS_COMPATIBLE__ = True
